Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease - Guo, Xiaoyi; Zhou, Wei; Yu, Yan; Cai, Yinghua; Zhang, Yuan; Du, Aiyan; Lu, Qun; Ding, Yijie; Li, Chao (Frontiers in Physiology, 2021)
Dry weight (DW) is an important dialysis index for patients with end stage renal disease (ESRD). It can guide clinical hemodialysis. Brain natriuretic peptide (BNP), chest computed tomography (CT) image, ultrasound and bioelectrical impedance analysis (BIA) are key indicators (multi-source information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we develops a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural network (RBFN), we propose a multiple Laplacian regularized RBFN model (MLapRBFN) to predict DW of patient. Compared with other model and body composition monitor (BCM), our method achieves the lowest value (1.3226) of root mean square error (RMSE). In Bland-Altman analysis of MLapRBFN, the number of out agreement interval are the least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employ an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied to clinical evaluation of DW for hemodialysis patients.